GLM 5.3 Flash vs Muse Spark Open
Updated October 21, 2026. 26 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick GLM 5.3 Flash for coding: it scores 71.0 on the Vibe Coding Index against 62.1 for Muse Spark Open.
Pick Muse Spark Open when what matters is Vibe Code Bench (Vals AI) or Code migration (Vals AI), where it leads.
- Shared results
- 26
- GLM 5.3 Flash only
- 76
- Muse Spark Open only
- 0
- Like-for-like categories
- 6 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, four public coding benchmarks
GLM 5.3 Flash
GLM 5.3 Flash scores 71.0 against 62.1.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
No call
Only GLM 5.3 Flash has a AA Intelligence Index result, 41.8.
Directional only
Agentic work
AA Agentic Index
No call
Only GLM 5.3 Flash has a AA Agentic Index result, 50.9.
Directional only
Reasoning
GPQA Diamond
No call
Only GLM 5.3 Flash has a GPQA Diamond result, 86.4%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 5.3 Flash has a MMLU Pro result, 86.1%.
Directional only
Web design
Design Arena: websites
Muse Spark Open
Muse Spark Open leads, 1311 against 1272.
Same board, both measured
General
Vals Index
No call
Only GLM 5.3 Flash has a Vals Index result, 50.9%.
Directional only
Long documents
Listed context window
No call
Both take 1.05M tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5.3 Flash
GLM 5.3 Flash $0.0004, Muse Spark Open $0.0034 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 5.3 Flash has the number, $0.02.
Directional only
At a glance
- Coding index
- GLM 5.3 Flash 71.0 from 2 benchmarks, Muse Spark Open 62.1 from 2 benchmarks.
- Widest category
- Coding, 18.9 points apart on average over 9 shared benchmarks.
- Widest single gap
- LiveBench browser: GLM 5.3 Flash 84.0% against Muse Spark Open 36.0%, 48.0 points apart on a 0 to 100 scale.
- Output price
- GLM 5.3 Flash $0.50 against Muse Spark Open $4.25 per million output tokens, 8.5x apart.
- Context window
- Both take 1.05M tokens.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | GLM 5.3 Flash | Bars | Muse Spark Open | Ties |
|---|---|---|---|---|
| Coding | 5 | 4 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 6 | 2 | ||
| Reasoning | 2 | 1 | ||
| Web design | 1 | 2 | ||
| General | 2 | 0 | ||
| All shared boards | 16 | 10 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index54
| Benchmark | GLM 5.3 Flash | Muse Spark Open | Leader |
|---|---|---|---|
| LiveBench coding | 80.6% | 73.6% | GLM 5.3 Flash |
| LMArena WebDev | 1607 | 1528 | GLM 5.3 Flash |
| AA Coding Index | 71.5 | Not measured | Only GLM 5.3 Flash measured |
| LiveCodeBench (Vals AI) | 80.5% | Not measured | Only GLM 5.3 Flash measured |
| SWE-bench Verified (Vals AI) | 92.0% | Not measured | Only GLM 5.3 Flash measured |
| Terminal-Bench 4 (Vals AI) | 37.4% | 6.6% | GLM 5.3 Flash |
| Vibe Code Bench (Vals AI) | 30.8% | 76.2% | Muse Spark Open |
| Code migration (Vals AI) | 20.5% | 21.2% | Muse Spark Open |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only GLM 5.3 Flash measured |
| IOI (Vals AI) | 52.5% | Not measured | Only GLM 5.3 Flash measured |
| Terminal-Bench 2.1 (Vals AI) | 62.9% | Not measured | Only GLM 5.3 Flash measured |
| Vibe Code Bench 1-100 (Vals AI) | 16.0% | Not measured | Only GLM 5.3 Flash measured |
| SkillsBench (Vals AI) | 40.2% | Not measured | Only GLM 5.3 Flash measured |
| CyberBench (Vals AI) | 67.9% | Not measured | Only GLM 5.3 Flash measured |
| SciCode | 51.6% | 58.4% | Muse Spark Open |
| FrontierCode | 31.8% | Not measured | Only GLM 5.3 Flash measured |
| FrontierSWE | 18.1% | Not measured | Only GLM 5.3 Flash measured |
| ALE-Bench | 303.6 | 1590.1 | Muse Spark Open |
| LMArena Text: coding | 1507 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: software and IT | 1500 | Not measured | Only GLM 5.3 Flash measured |
| LMArena WebDev: React | 1624 | 1529 | GLM 5.3 Flash |
| LMArena WebDev: HTML | 1577 | 1495 | GLM 5.3 Flash |
| LMArena WebDev: from an image | 1578 | Not measured | Only GLM 5.3 Flash measured |
| Neither model has a result on the other 15 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 5.3 Flash | Muse Spark Open | Leader |
|---|---|---|---|
| AA Intelligence Index | 41.8 | Not measured | Only GLM 5.3 Flash measured |
| Epoch Capabilities Index | 153.1 | 155.1 | Muse Spark Open |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index62
| Benchmark | GLM 5.3 Flash | Muse Spark Open | Leader |
|---|---|---|---|
| AA Agentic Index | 50.9 | Not measured | Only GLM 5.3 Flash measured |
| LiveBench research | 70.1% | 80.3% | Muse Spark Open |
| LiveBench browser | 84.0% | 36.0% | GLM 5.3 Flash |
| LiveBench long agentic | 28.4% | 18.9% | GLM 5.3 Flash |
| SREBench (Vals AI) | 0.8% | Not measured | Only GLM 5.3 Flash measured |
| Finance Agent (Vals AI) | 57.9% | 61.3% | Muse Spark Open |
| Tax Agent Bench (Vals AI) | 31.6% | 26.3% | GLM 5.3 Flash |
| Legal Research Bench (Vals AI) | 45.2% | 38.5% | GLM 5.3 Flash |
| Harvey's Legal Agent Benchmark (Vals AI) | 10.4% | Not measured | Only GLM 5.3 Flash measured |
| Excel Modeling Benchmark (Vals AI) | 55.9% | 53.7% | GLM 5.3 Flash |
| Terminal-Bench-Science (Vals AI) | 2.9% | Not measured | Only GLM 5.3 Flash measured |
| APEX-Agents | 52.8% | 33.4% | GLM 5.3 Flash |
| Neither model has a result on the other 27 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index21
| Benchmark | GLM 5.3 Flash | Muse Spark Open | Leader |
|---|---|---|---|
| GPQA Diamond | 86.4% | Not measured | Only GLM 5.3 Flash measured |
| LiveBench reasoning | 77.6% | 73.7% | GLM 5.3 Flash |
| MMMU Pro (Vals AI) | 86.0% | Not measured | Only GLM 5.3 Flash measured |
| GPQA Diamond (Artificial Analysis run) | 91.2% | 89.0% | GLM 5.3 Flash |
| Humanity's Last Exam (Artificial Analysis run) | 39.9% | 43.9% | Muse Spark Open |
| GPQA Diamond (Epoch AI run) | 90.2% | Not measured | Only GLM 5.3 Flash measured |
| ARC-AGI | 91.0% | Not measured | Only GLM 5.3 Flash measured |
| ARC-AGI-2 | 65.8% | Not measured | Only GLM 5.3 Flash measured |
| Chess puzzles | 21.0% | Not measured | Only GLM 5.3 Flash measured |
| Mystery game puzzles | 8.0% | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: hard prompts | 1490 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: expert | 1511 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: hard prompts in English | 1488 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Vision: diagrams | 1317 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Vision: homework | 1318 | Not measured | Only GLM 5.3 Flash measured |
| Neither model has a result on the other 6 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GLM 5.3 Flash | Muse Spark Open | Leader |
|---|---|---|---|
| OTIS Mock AIME | 93.9% | Not measured | Only GLM 5.3 Flash measured |
| FrontierMath, tiers 1 to 3 | 55.8% | Not measured | Only GLM 5.3 Flash measured |
| FrontierMath, tier 4 | 17.1% | Not measured | Only GLM 5.3 Flash measured |
| ProofBench | 21.0% | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: math | 1503 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: mathematical work | 1497 | Not measured | Only GLM 5.3 Flash measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GLM 5.3 Flash | Muse Spark Open | Leader |
|---|---|---|---|
| MMLU Pro | 86.1% | Not measured | Only GLM 5.3 Flash measured |
| LegalBench (Vals AI) | 83.9% | Not measured | Only GLM 5.3 Flash measured |
| TaxEval (Vals AI) | 75.6% | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: science | 1484 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: medicine and healthcare | 1475 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: legal and government | 1473 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: business and finance | 1460 | Not measured | Only GLM 5.3 Flash measured |
| Neither model has a result on the other 10 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index12
| Benchmark | GLM 5.3 Flash | Muse Spark Open | Leader |
|---|---|---|---|
| Design Arena: websites | 1272 | 1311 | Muse Spark Open |
| Design Arena: UI components | 1316 | Not measured | Only GLM 5.3 Flash measured |
| Design Arena: game dev | 1279 | 1290 | Muse Spark Open |
| Design Arena: data viz | 1262 | Not measured | Only GLM 5.3 Flash measured |
| Design Arena: 3D | 1316 | Not measured | Only GLM 5.3 Flash measured |
| Design Arena: all code categories | 1282 | Not measured | Only GLM 5.3 Flash measured |
| Design Arena: SVG | 1284 | Not measured | Only GLM 5.3 Flash measured |
| Design Arena: ASCII art | 1269 | Not measured | Only GLM 5.3 Flash measured |
| LiveBench design | 77.2% | 60.3% | GLM 5.3 Flash |
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 5.3 Flash | Muse Spark Open | Leader |
|---|---|---|---|
| Vals Index | 50.9% | Not measured | Only GLM 5.3 Flash measured |
| LiveBench data analysis | 76.4% | 60.2% | GLM 5.3 Flash |
| MedScribe (Vals AI) | 88.9% | Not measured | Only GLM 5.3 Flash measured |
| Long context reasoning (AA-LCR) | 80.0% | 78.3% | GLM 5.3 Flash |
| Surface Evolver Bench | 52.5% | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text | 1471 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: instruction following | 1478 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: creative writing | 1441 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Vision | 1294 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: English | 1474 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: non-English | 1462 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: Chinese | 1531 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: German | 1462 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: Japanese | 1444 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: Korean | 1436 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: French | 1491 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: Spanish | 1475 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: Russian | 1465 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: Polish | 1450 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: multi-turn | 1469 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: longer queries | 1481 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: writing and language | 1455 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: entertainment and media | 1440 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Vision: English | 1295 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Vision: Chinese | 1363 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Vision: reading text | 1299 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Vision: humor | 1279 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Vision: creative writing | 1270 | Not measured | Only GLM 5.3 Flash measured |
| Neither model has a result on the other 18 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.3 Flash | Muse Spark Open | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
GLM 5.3 Flash costs less
Repository review
50K input + 3K output tokens
GLM 5.3 Flash costs less
Agent loop
200K input + 10K output tokens
GLM 5.3 Flash costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | Z.ai GLM 5.3 Flash | Meta Muse Spark Open |
|---|---|---|
| Maker | Z.ai | Meta |
| Weights | Open weights | Open weights |
| Released | August 26, 2026 | October 15, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $0.15 | $1.25 |
| Output $/Mtok | $0.50 | $4.25 |
| Coding index | 71.0 | 62.1 |
| Independent measurements behind it | 2 | 2 |
| Cost per solved task | $0.02 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.3 Flash better than Muse Spark Open for coding?
Pick GLM 5.3 Flash for coding: it scores 71.0 on the Vibe Coding Index against 62.1 for Muse Spark Open. Pick Muse Spark Open when what matters is Vibe Code Bench (Vals AI) or Code migration (Vals AI), where it leads.
Which is cheaper, GLM 5.3 Flash or Muse Spark Open?
GLM 5.3 Flash costs $0.15 per million input tokens and $0.50 per million output tokens; Muse Spark Open costs $1.25 and $4.25. Cost per solved task is known for GLM 5.3 Flash only.
Which has the bigger context window, GLM 5.3 Flash or Muse Spark Open?
Neither: both take 1.05M tokens.
How many benchmarks is the GLM 5.3 Flash vs Muse Spark Open comparison based on?
26 benchmarks have a result for both models as of October 21, 2026. GLM 5.3 Flash's coding index rests on 2 independent benchmarks and Muse Spark Open's on 2. Where only one model has a result, the page says so instead of naming a winner.